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Digital supply chains require nascent technologies like blockchain and Internet of Things (IoT). There is a need to develop a roadmap for the implementation of these technologies, as they require a huge amount of resources and infrastructure. The purpose of this paper is to analyze the challenges of implementing blockchain-IoT integrated architecture in the green supply chain and develop strategies for the same.

After a thorough literature survey of Scopus-indexed journals and books, 37 barriers were identified, which were then brought down to 15 barriers after confirming with industry and academic experts using the Delphi method. Using the total interpretive structural modeling (TISM) method and cross-impact matrix multiplication applied to classification (MICMAC) analysis, the barriers were modeled, and finally, strategies were formulated using a concept map to handle the barriers in the blockchain-IoT integrated architecture for a green supply chain.

This paper presents the research on barriers that can be considered for incorporating blockchain and IoT in the green supply chain. It was found from the TISM model that environmental concerns are Level-1 barriers and need to be addressed by developing appropriate technology and allocating funds for the same. An integrated ecosystem with blockchain and IoT is developed.

The focus of this study was on the challenges of blockchain and IoT; hence, it is required to extend the research and find challenges for different industries and also analyze the criteria using other multi-criteria decision-making (MCDM) methods. Further research is required for the integration of blockchain-IoT with supply chain functions.

The transformation of a traditional supply chain into a green supply chain is possible with the integration of technologies. This research work and the strategies developed are useful to managers and practitioners working on technology implementation. Planning resources and addressing key barriers is possible with the concept maps and architecture developed.

Green supply chain management (SCM) is gaining importance in industry as well as the academic sector due to government Policies and norms worldwide for reducing emissions and encouraging environment-friendly production systems. Incorporating blockchain and IoT in a green supply chain will further digitize and increase transparency in supply chains.

We have done a categorization of all barriers based on the expert survey by academicians and industry experts from industries in India. The concept map helps in identifying possible solutions for the challenges and initiatives to be taken for the smooth integration of technologies in the green supply chain.

A reconfigurable manufacturing system (RMS) can provide manufacturing flexibility, meet changing market demands and deliver high performance, among other benefits. However, adoption and performance improvement are critical activities in it. The current study aims to identify the important factors influencing RMS adoption and validate a conceptual model as well as develop a structural model for the identified factors.

An extensive review of RMS articles was conducted to identify the eight factors and 47 sub-factors that are relevant to RMS adoption and performance improvement. For these factors, a conceptual framework was developed as well as research hypotheses were framed. A questionnaire was developed, and 117 responses from national and international domain experts were collected. To validate the developed framework and test the research hypothesis, structural equation modeling was used, with software tools SPSS and AMOS.

The findings support six hypotheses: “advanced technologies,” “quality and safety practice,” “strategy and policy practice,” “organizational practices,” “process management practices,” and “soft computing practices.” All of the supported hypotheses have a positive impact on RMS adoption. However, the two more positive hypotheses, namely, “sustainability practices” and “human resource policies,” were not supported in the analysis, highlighting the need for greater awareness of them in the manufacturing community.

The current study is limited to the 47 identified factors; however, these factors can be further explored and more sub-factors identified, which are not taken into account in this study.

Managers and practitioners can use the current work’s findings to develop effective RMS implementation strategies. The results can also be used to improve the manufacturing system’s performance and identify the source of poor performance.

This paper identifies critical RMS adoption factors and demonstrates an effective structural-based modeling method. This can be used in a variety of fields to assist policymakers and practitioners in selecting and implementing the best manufacturing system.

With big data (BD), traditional supply chain is shifting to digital supply chain. This study aims to address the issues and challenges in the way toward the implementation of big data analytics (BDA) in sustainable supply chain management (SSCM).

The factors that affect the implementation of BDA in SSCM are identified through a widespread literature review. The PESTEL framework is used for this purpose as it covers all the political, economic, social, technological, environmental and legal factors. These factors are then finalized by means of experts' opinion and analyzed using structural equation modeling (SEM).

A total of 10 factors are finalized with 31 sub-factors, of which sustainable performance, competitive advantage, stakeholders' involvement and capabilities, lean and green practices and improvement in environmental performance are found to be the critical factors for the implementation of BDA in SSCM.

This research has taken up the case of Indian manufacturing industry. It can be diversified to other geographical areas and industry sectors. Further, the quantitative analysis may be undertaken with structured or semi-structured interviews for validation of the proposed model.

This research provides an insight to managers regarding the implementation of BDA in SSCM by identifying and examining the influencing factors. The results may be useful for managers for the implementation of BDA and budget allocation for BDA project.

The result includes green practices and environmental performance as critical factors for the implementation of BDA in SSCM. Thus the research establishes a positive relationship between BDA and sustainable manufacturing that ultimately benefits the environment and society.

This research addresses the challenges in the implementation of BDA in SSCM in Indian manufacturing sector, where such application is at its nascent stage. The use of PESTEL framework for identifying and categorizing the factors makes the study more worthwhile, as it covers full spectrum of the various factors that affect the strategic business decisions.

Agile new product development (ANPD) attracts researchers and practitioners by its ability to rapidly reconfigure products and related processes to meet the needs of emerging markets. To increase ANPD adoption, this study aims to identify ANPD enablers (ANPDEs) and create a structural framework that practitioners can use as a quick reference.

Initially, a comprehensive literature review is conducted to identify ANPDEs, and a structural framework is developed in consultation with an expert panel using a hybrid robust best–worst method interpretive structural modeling (ISM). During the ISM process, the interactions between the ANPDEs are investigated. The ISM result is used as input for fuzzy Matrice d’Impacts croises-multiplication appliqúean classment means cross-impact matrix multiplication applied to classification (MICMAC) analysis to investigate enablers that are both strong drivers and highly dependent.

The study’s findings show that four ANPDEs are in the low-intensity cluster and thus are excluded during the structural frame development. ISM output shows that “Strong commitment to NPD/top management support,” “Availability of resources,” “Supplier commitment/capability” and “Systematic project planning” are the important ANPDEs. Based on their driving and dependence power, the clusters formed during the fuzzy MICMAC approach show that 16 ANPDEs appear in the dependent zone, one ANPDE in the linkage zone and 14 ANPDEs in the driving zone.

This research has intense functional consequences for researchers and practitioners within the industry. Industry professionals require a conservative focus on the established ANPDEs during ANPD adoption. Management has to carefully prepare a course of action to avoid any flop during ANPD adoption.

The framework established is a one-of-a-kind study that provides an integrated impression of important ANPDEs. The authors hope that the suggested structural framework will serve as a blueprint for scholars working in the ANPD domain and will aid in its adoption.

The purpose of this study was to investigate the likely causes of failure of some sections of road pavements in Ajaokuta, Northcentral Nigeria. This was achieved through a geotechnical assessment of subgrade soils in affected areas.

The methods entailed field and laboratory methods and statistical analysis. Subgrade soil samples were retrieved from a depth of 1,000 mm beneath the failed portions using a hang auger. The soils were analyzed for natural moisture content (NMC), Atterberg limit (liquid limit, plastic limit and linear shrinkage), grain size distribution, compaction and California bearing ratio (CBR), respectively.

The results of the geotechnical tests ranged from NMC (12.5%–19.4%), sand (84%–98%), fines (2%–16%), LL (16.0%–32.2%), PL (17%–27.5%), LS (2.7%–6.4%), PI (2.5%–18.4%), maximum dry density (1756 kg/m2–1961 kg/m2), optimum moisture content (13.2%–20.2%), unsoaked CBR (15.5%–30.5%) and soaked CBR (8%–22%), respectively. Pearson’s correlation coefficient performed on the variables showed that some parameters exhibited a strong positive correlation with r2 > 0.5.

Funding was the main limitation.

Comparing the results with Nigerian standards for road construction, and the AASHTO classification scheme, the subgrade soils are competent and possess excellent to good properties. The soils also exhibited very low plasticity, a high percentage of sand, high CBR and low NMC, which implies that it has the strength required for road pavement subgrades. The likely causes of the failures are, therefore, due to the use of poor construction materials, technical incompetence and poor compaction of sub-base materials, respectively.

Electric cars have very little market share in developing countries despite their environmental benefits. Thus, governments have started promoting electric cars by providing financial incentives to consumers. The current article aims to examine the direct and indirect effects of government financial incentives on consumer electric car adoption in India.

The study followed a quantitative research method that employed a self-administered survey questionnaire. Structural Equation Modelling and Multi-Group Analysis were followed for data analysis.

The study revealed that financial incentives have an indirect effect on electric car adoption intention rather than a direct effect. In addition, financial incentives were found to have a direct effect on attitude and Perceived Behavioural Control (PBC). Attitude and PBC positively influenced consumer adoption intention.

The insights and implications from the present study would help policymakers and marketers to formulate better incentive policies and market strategies to increase consumer acceptance of electric cars in developing countries.

The study contributes to the literature by analysing the underlying mechanism that links government financial incentives to electric car adoption intention. This study also explored the direct effect of financial incentives on attitude and PBC, which are less investigated in electric vehicle literature. In addition, the present article also assessed the moderating role of age in electric car adoption, which has mixed evidence in the literature, and such studies are scarce in the Indian context.

The purpose of this study is to present a systematic and comprehensive review of personalized, adaptive and semantic e-learning systems.

Preferred reporting items of systematic reviews and meta-analyses guidelines have been used for a thorough insight into associated aspects of e-learning that complement the e-learning pedagogies and processes. The aspects of e-learning systems have been reviewed comprehensively such as personalization and adaptivity, e-learning and semantics, learner profiling and learner categorization, which are handy in intelligent content recommendations for learners.

The adoption of semantic Web based technologies would complement the learner’s performance in terms of learning outcomes.

The evaluation of the proposed framework depends upon the yearly batch of learners and recording is a cumbersome/tedious process.

E-Learning systems may have diverse and positive impact on society including democratized learning and inclusivity regardless of socio-economic or geographic status.

A preliminary framework of an ontology-based e-learning system has been proposed at a modular level of granularity for implementation, along with evaluation metrics followed by a future roadmap.

Understanding consumer behaviour towards ethanol-blended fuel is crucial for assessing the adoption of alternate fuel vehicles (AFVs). By examining factors influencing purchase decisions, such as cost considerations, environmental concerns and perceptions of vehicle performance, researchers can elucidate patterns of consumer acceptance and identify barriers to widespread adoption.

The study uses the theory of planned behaviour (TPB) and stimulus organism response (S-O-R). The current research aims to bridge the gap by focusing on consumers' intention to buy ethanol-blended fuel. Data were collected from 303 survey respondents and analysed using partial least square structural equation modelling (PLS-SEM).

The study finds that adopting motivation, policy incentives, risk perception and sustainable practices significantly influence the intention to purchase ethanol-blended fuel. Surprisingly, cost perception and infrastructure readiness do not have a significant impact on purchase intention.

The study proposes four innovative policy implications to amplify the adoption of ethanol-blended fuel. These policies are (a) flexible fuel vehicle incentive schemes, (b) renewable fuel mandate and certification, (c) ethanol pricing and infrastructure development policy and (d) ethanol urban mobility and public transportation initiatives.

This study provides novel insights into the factors influencing ethanol-blended fuel adoption in India, contributing to the literature on sustainable transportation solutions.

Transportation-related pollution is expected to decrease when using battery electric cars. This will not only address energy and environmental issues but also promote reform and transformation in the zero-emission automotive industry. To craft policy interventions and promotional initiatives, manufacturers need to comprehend the techno-psychological perspectives of automotive users on the adoption of electric cars. Therefore, this study aims to test a “perception-attitude-intention” linking framework built upon the “Unified Theory of Technology Acceptance and Use” (UTAUT) and analyze the behavioral intentions of existing automobile users to embrace battery electric cars.

The conceptual model tests the underlying direct paths, the mediation of attitudes and the moderating gender effects in predicting users’ attitudes and behavioral intentions to adopt battery electric cars using a techno-psychological approach from UTAUT. “Structural equation modeling” is used to analyze the model using the 361 valid online responses received from conventional car owners.

The results show that behavioral intentions are directly predicted by UTAUT measures with attitudes and indirectly through its mediation and gender moderation. The results support the “Perceptions-Attitudes-Intentions” linkage model that explains the phenomenon of electric car adoption. However, the mediating and moderating paths between facilitating conditions and intentions do not support the model. In addition, the research corroborates that men have a stronger effect than women on behavioral intentions to prefer battery electric cars.

This work may assist manufacturers and regulators in developing marketing policies to encourage consumers’ adoption of battery electric cars and potentially improve their favorable perception of these vehicles.

This study contributes to the comprehension of how UTAUT constructs shape consumers’ attitudes and behavioral intentions regarding the adoption of battery cars equipped with emission-free technology. This study validates the grounded framework “perception-attitude-intention” linkage model, which also describes gender-wise differences toward electric car adoption in the backdrop of Indian sustainable transportation.

The purpose of this study is to identify and validate the key consumer-related factors influencing consumer attitude toward electric vehicles (EVs) and intention to purchase EVs. The role of consumer attitude in mediating consumer-related factors and intention to buy is also explored in this study. Consumer-related factors considered in this study include environmental orientation (EO), new technology orientation (NTO), social orientation (SO) and perceived monetary benefits (PMB).

Data for the study were collected from individuals who do not own an EV in Chennai, Tamil Nadu, India. Data were collected from 388 respondents using Google Forms. Convenience sampling technique was used to identify the respondents. Both descriptive and inferential statistics techniques were used to analyze data collected.

The study identified EO, SO and PMB as antecedents of attitude (ATT) toward EV and NTO, SO and PMB as potential drivers of EV purchase intention (PI). In addition, the authors also observe that EO is fully mediated by ATT toward consumer PI, whereas ATT partially mediates the relationship between the independent variables SO and PMB and the dependent variable PI.

EVs are at the forefront of automotive innovation. Automobile industries producing EVs showcase a commitment to embracing cutting-edge technology, positioning their companies as forward-thinking and technologically advanced. Creating awareness about EV technology, dispelling myths about range anxiety and charging times should be done methodologically. The lower operating costs of EVs and the government policies that reduce the total cost of ownership can lead to faster market penetration. The widespread adoption of EVs can contribute to a nation’s economic growth by adding new businesses in the industry, infrastructure development and job creation.

The reduced environmental impact of EVs, such as improved air quality and reduced greenhouse gas emissions, can lead to significant societal benefits in terms of better human health and mitigating the effects of climate change. Governments can leverage these benefits to achieve climate targets and improve public health.

Studies about EV diffusion and adoption in India are yet to gain momentum considering the slow penetration of EVs in the Indian automobile market. The Indian Government is enforcing policies and incentives to accelerate the adoption of EVs. However, without understanding the drivers of consumer EV adoption, it is difficult for policymakers to accelerate the growth of the EV market. The present study is one step toward understanding the same in its original sense.